Impact of Patient‐Centered Discharge Tools: A Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Patient-centered discharge tools provide an opportunity to engage patients, enhance patient understanding, and improve capacity for self-care and postdischarge outcomes. PURPOSE: To review studies that engaged patients in the design or delivery of discharge instruction tools and that tested their effect among hospitalized patients. DATA SOURCES: We conducted a search of 12 databases and journals from January 1994 through May 2014, and references of retrieved studies. STUDY SELECTION: English-language studies that tested discharge tools meant to engage patients were selected. Studies that measured outcomes after 3 months or without a control group or period were excluded. DATA EXTRACTION: Two independent reviewers assessed the full-text papers and extracted data on features of patient engagement. DATA SYNTHESIS: Thirty articles met inclusion criteria, 28 of which examined educational tools. Of these, 13 articles involved patients in content creation or tool delivery, with only 6 studies involving patients in both. While many of these studies (10 studies) demonstrated an improvement in patient comprehension, few studies found improvement in patient adherence despite their engagement. A few studies demonstrated an improvement in self-efficacy (2 studies) and a reduction in unplanned visits (3 studies). CONCLUSIONS: Improving patient engagement through the use of media, visual aids, or by involving patients when creating or delivering a discharge tool improves comprehension. However, further studies are needed to clarify the effect on patient experience, adherence, and healthcare utilization postdischarge. Better characterization of the level of patient engagement when designing discharge tools is needed given the heterogeneity found in current studies. Journal of Hospital Medicine 2017;12:110-117.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it